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How to check whether an open-weight model fits your machine before you download it with Vibe Service Guardian

Compare physical load limits, practical limits, and target SLA limits for a planned user count and concurrency.

Short answer: Start the console with python -m vsg --open; then open the model capacity page and let it read CPU, memory, disk, GPU or unified memory, and installed local runtimes; Enter planned users, peak concurrency, average input and output tokens, per-session context, target tokens per second per user, and target time to first token. All steps.

Who it is forA developer or team lead deciding whether a local open-weight model can serve a planned number of users on a specific laptop, workstation, or GPU box.

The problemModel file size alone does not show whether KV cache, concurrency, token speed, and time to first token will fit in the memory that is actually free.

The outcomeYou get three separate answers: the physical load ceiling, the practical ceiling with a minimum speed, and the ceiling that meets your target. Each candidate shows weights, KV cache, workspace, and risk reasons.

What you need first

  • Python 3.10 to 3.12 installed on Windows, macOS, or Linux
  • A local copy of the repo from https://github.com/keith-yan/vibe-service-guardian
  • Permission to run a local web console bound to 127.0.0.1

Steps

  1. Start the console with python -m vsg --open.
  2. Open the model capacity page and let it read CPU, memory, disk, GPU or unified memory, and installed local runtimes.
  3. Enter planned users, peak concurrency, average input and output tokens, per-session context, target tokens per second per user, and target time to first token.
  4. Review each candidate's weights, KV cache, workspace, per-user throughput range, and the maximum concurrency under target.
  5. Copy the generated run command template, which is fixed to 127.0.0.1 and is not executed automatically, and test it on the real machine before relying on it.

Prompt for your agent

Using keith-yan/vibe-service-guardian, open the model capacity page with `python -m vsg --open`, enter the plan for <number of users> users at <peak concurrency> concurrency, and report the physical, practical, and target SLA ceilings for <model name>. Verify that each figure lists its risk reasons and that the result is treated as an estimate until a local benchmark is run.

Caveat. The capacity output is a planning estimate and does not replace a same-load benchmark, and the built-in model catalog is a dated offline snapshot rather than a full list.

Use this in your AI agent

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Using the Deduped MCP, get the setup steps for keith-yan/vibe-service-guardian and "Check whether an open-weight model fits your machine before you download it"

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